To add to what I mentioned through chat: I’m primarily thinking about using some of what I have in the ID Lab for the 2D object recognition challenge. In this case my best guess is that (via hexagonally arranged minicolumn network?) the columns already have some ability to control motors for self-exploring a simple environment.
I would essentially add missing layers, using ones that need 3 axis of interconnection to get optimal 3.5/6 radiation pattern, possibly being detected in this earlier shown illustration.
In my code the reason for 3.5 is that negating received signals to derive output action results in the number of wave passing outputs alternating between 3 and 4. The back and forth jitter is in the math of the geometry. When readings temporally average together the 1 of 6 direction resolution becomes 1 of 12 or more.
A 2 axis system presents a wave generation problem that is thankfully not an issue for real (mini)columns that close pack together into a more convenient 3 axis geometry. It’s then easy to broadcast a roundish energy efficient signal outwards in directions, without signals easily returning back in the direction they came and associated signal chaos.
Although 2 axis is much easier to code the 3 axis geometry has too many advantages, so I’m back to hexagons again.
It’s possible to convert back and forth, in which case starting with the Cube coordinate system is here recommended, but in the illustration the Y is annoyingly opposite from usual screen direction and would like to reverse that:
It would be a big help for at least myself for there to be one recommended way to articulate hexagonally arranged connections and places, then convert to 2 axis floating point coordinates and back to 3 axis again. The subroutines I wrote for VB6 can do all that, but Python examples I have seen might easily be a big improvement.